The Explanatory Potential of Artificial Societies

It is often claimed that artificial society simulations contribute to the explanation of social phenomena. At the hand of a particular example, this paper argues that artificial societies often cannot provide full explanations, because their models are not or cannot be validated. Instead, artificial societies may sometimes provide potential explanations. It is shown that these potential explanations, if they contribute to our understanding, considerably differ from the standard kind of potential causal explanations. Instead of possible causal histories, simulations offer possible functional analyses of the explanandum. The paper discusses how these two kinds of potential explanations differ, and how possible functional analyses can be appraised

Emerging Artificial Societies Through Learning

The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs.Artificial Societies, Evolution of Language, Decision Trees, Peer-To-Peer Networks, Social Learning

Environment design for emerging artificial societies

The NewTies project is developing a system in which societies of agents are expected to develop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve the environmental challenges that they are set by acting collectively. The challenges are intended to be analogous to those faced by early, simple, small-scale human societies. Some issues in the construction of a virtual environment for the system are described and it is argued that multi-agent social simulation has so far tended to neglect the importance of environment design.agent-based modelling, stone age economics, economic anthropolgy

Artificiality in Social Sciences

This text provides with an introduction to the modern approach of artificiality and simulation in social sciences. It presents the relationship between complexity and artificiality, before introducing the field of artificial societies which greatly benefited from the computer power fast increase, gifting social sciences with formalization and experimentation tools previously owned by "hard" sciences alone. It shows that as "a new way of doing social sciences", artificial societies should undoubtedly contribute to a renewed approach in the study of sociality and should play a significant part in the elaboration of original theories of social phenomena.artificial societies; multi-agent systems; distributed artificial intelligence; complexity

The Ribbon of Love: Fuzzy-Ruled Agents in Artificial Societies

The paper brings two motivations to the theoretical explorations of social analysis. The first is to enrich the agent based computational sociology by incorporating the fuzzy set theory in to the computational modeling. This is conducted by showing the importance to include the fuzziness into artificial agent’s considerations and her way acquiring and articulate information. This is continued with the second motives to bring the Darwinian sexual selection theory – as it has been developed broadly in evolutionary psychology – into the analysis of social system including cultural analysis and other broad aspects of sociological fields. The two was combined in one computational model construction showing the fuzziness of mating choice, and how to have computational tools to explain broad fields of social realms. The paper ends with some opened further computer program development

Book Review of Joshua M. Epstein & Robert Axtell, Growing Artificial Societies: Social Science from the Bottom Up (MIT Press 1996)

Review of the book Joshua M. Epstein & Robert Axtell, Growing Artificial Societies: Social Science from the Bottom Up (MIT Press 1996). Acknowledgements, appendices, index, introduction references. LC 96-25332; ISBN 0-262-05053-6. [208 pp. Cloth $39.95. 55 Hayward St., Cambridge MA 02142.

Emerging Artificial Societies Through Learning

The NewTies project is implementing a simulation in which societies of agents are expected to de-velop autonomously as a result of individual, population and social learning. These societies are expected to be able to solve environmental challenges by acting collectively. The challenges are in-tended to be analogous to those faced by early, simple, small-scale human societies. This report on work in progress outlines the major features of the system as it is currently conceived within the project, including the design of the agents, the environment, the mechanism for the evolution of language and the peer-to-peer infrastructure on which the simulation runs

Artificial Societies of Intelligent Agents

In this thesis we present our work, where we developed artificial societies of intelligent agents, in order to understand and simulate adaptive behaviour and social processes. We obtain this in three parallel ways: First, we present a behaviours production system capable of reproducing a high number of properties of adaptive behaviour and of exhibiting emergent lower cognition. Second, we introduce a simple model for social action, obtaining emergent complex social processes from simple interactions of imitation and induction of behaviours in agents. And third, we present our approximation to a behaviours virtual laboratory, integrating our behaviours production system and our social action model in animats. In our behaviours virtual laboratory, the user can perform a wide variety of experiments, allowing him or her to test the properties of our behaviours production system and our social action model, and also to understand adaptive and social behaviour. It can be accessed and downloaded through the Internet. Before presenting our proposals, we make an introduction to artificial intelligence and behaviour-based systems, and also we give notions of complex systems and artificial societies. In the last chapter of the thesis, we present experiments carried out in our behaviours virtual laboratory showing the main properties of our behaviours production system, of our social action model, and of our behaviours virtual laboratory itself. Finally, we discuss about the understanding of adaptive behaviour as a path for understanding cognition and its evolution

Expectation-driven interaction: a model based on Luhmann's contingency approach

We introduce an agent-based model of interaction, drawing on the contingency approach from Luhmann's theory of social systems. The agent interactions are defined by the exchange of distinct messages. Message selection is based on the history of the interaction and developed within the confines of the problem of double contingency. We examine interaction strategies in the light of the message-exchange description using analytical and computational methods.Comment: 37 pages, 16 Figures, to appear in Journal of Artificial Societies and Social Simulation